A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory. Nilsson, M., Nordkvist, K., Jonzén, J., Lindgren, N., Axensten, P., Wallerman, J., Egberth, M., Larsson, S., Nilsson, L., Eriksson, J., & Olsson, H. Remote Sensing of Environment, 2017. ![A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The National Mapping Agency in Sweden has conducted an airborne laser scanning (ALS) campaign covering almost the entire country for the purpose of creating a new national Digital Elevation Model (DEM). The ALS data were collected between 2009 and 2015 using Leica, Optech, Riegl, and Trimble scanners and have a point density of 0.5–1.0 pulses/m2. A high resolution national raster database (12.5 m × 12.5 m cell size) with forest variables was produced by combining the ALS data with field data from the Swedish National Forest Inventory (NFI). Approximately 11500 NFI plots (10 meter radius) located on productive forest land, inventoried between 2009 and 2013, were used to create linear regression models relating selected forest variables, or transformations of the variables, to metrics derived from the ALS data. The resulting stand level relative RMSEs for predictions of stem volume, basal area, basal-area weighted mean tree height, and basal-area weighted mean stem diameter were in the ranges of 17.2–22.0%, 13.9–18.2%, 5.4–9.5%, and 8.7–13.1%, respectively. It was concluded that the predictions had an accuracy that were at least as good as data typically used in forest management planning. Above ground tree biomass was also included in the national raster database but not validated on a stand-level. An important part of the project was to make the raster database available to private forest owners, forest associations, forest companies, authorities, researchers, and the general public. Thus, all predicted forest variables can be viewed and downloaded free of charge at the Swedish Forest Agency's homepage (http://www.skogsstyrelsen.se/skogligagrunddata).
@article{RN638,
author = {Nilsson, Mats and Nordkvist, Karin and Jonzén, Jonas and Lindgren, Nils and Axensten, Peder and Wallerman, Jörgen and Egberth, Mikael and Larsson, Svante and Nilsson, Liselott and Eriksson, Johan and Olsson, Håkan},
title = {A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory},
journal = {Remote Sensing of Environment},
abstract = {The National Mapping Agency in Sweden has conducted an airborne laser scanning (ALS) campaign covering almost the entire country for the purpose of creating a new national Digital Elevation Model (DEM). The ALS data were collected between 2009 and 2015 using Leica, Optech, Riegl, and Trimble scanners and have a point density of 0.5–1.0 pulses/m2. A high resolution national raster database (12.5 m × 12.5 m cell size) with forest variables was produced by combining the ALS data with field data from the Swedish National Forest Inventory (NFI). Approximately 11500 NFI plots (10 meter radius) located on productive forest land, inventoried between 2009 and 2013, were used to create linear regression models relating selected forest variables, or transformations of the variables, to metrics derived from the ALS data. The resulting stand level relative RMSEs for predictions of stem volume, basal area, basal-area weighted mean tree height, and basal-area weighted mean stem diameter were in the ranges of 17.2–22.0%, 13.9–18.2%, 5.4–9.5%, and 8.7–13.1%, respectively. It was concluded that the predictions had an accuracy that were at least as good as data typically used in forest management planning. Above ground tree biomass was also included in the national raster database but not validated on a stand-level. An important part of the project was to make the raster database available to private forest owners, forest associations, forest companies, authorities, researchers, and the general public. Thus, all predicted forest variables can be viewed and downloaded free of charge at the Swedish Forest Agency's homepage (http://www.skogsstyrelsen.se/skogligagrunddata).},
keywords = {Nationwide forest database
National forest inventory
Airborne laser scanning},
ISSN = {0034-4257},
DOI = {10.1016/j.rse.2016.10.022},
url = {http://dx.doi.org/10.1016/j.rse.2016.10.022},
year = {2017},
type = {Journal Article}
}
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The ALS data were collected between 2009 and 2015 using Leica, Optech, Riegl, and Trimble scanners and have a point density of 0.5–1.0 pulses/m2. A high resolution national raster database (12.5 m × 12.5 m cell size) with forest variables was produced by combining the ALS data with field data from the Swedish National Forest Inventory (NFI). Approximately 11500 NFI plots (10 meter radius) located on productive forest land, inventoried between 2009 and 2013, were used to create linear regression models relating selected forest variables, or transformations of the variables, to metrics derived from the ALS data. The resulting stand level relative RMSEs for predictions of stem volume, basal area, basal-area weighted mean tree height, and basal-area weighted mean stem diameter were in the ranges of 17.2–22.0%, 13.9–18.2%, 5.4–9.5%, and 8.7–13.1%, respectively. It was concluded that the predictions had an accuracy that were at least as good as data typically used in forest management planning. Above ground tree biomass was also included in the national raster database but not validated on a stand-level. An important part of the project was to make the raster database available to private forest owners, forest associations, forest companies, authorities, researchers, and the general public. 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The ALS data were collected between 2009 and 2015 using Leica, Optech, Riegl, and Trimble scanners and have a point density of 0.5–1.0 pulses/m2. A high resolution national raster database (12.5 m × 12.5 m cell size) with forest variables was produced by combining the ALS data with field data from the Swedish National Forest Inventory (NFI). Approximately 11500 NFI plots (10 meter radius) located on productive forest land, inventoried between 2009 and 2013, were used to create linear regression models relating selected forest variables, or transformations of the variables, to metrics derived from the ALS data. The resulting stand level relative RMSEs for predictions of stem volume, basal area, basal-area weighted mean tree height, and basal-area weighted mean stem diameter were in the ranges of 17.2–22.0%, 13.9–18.2%, 5.4–9.5%, and 8.7–13.1%, respectively. 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